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開発者向け MCP サーバーとよく使われる AI コードエディタ。
MCP Servers
MCP-Universe is a comprehensive framework designed for RL training, benchmarking, and developing AI agents for general tool-use.
The safest way to make REST calls in C# with an MCP Generator
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The official GitHub Copilot plugins collection ✨
Discover Exceptional MCP Servers
A Model Context Protocol server built with Express.js that provides cryptographic tools including key pair generation, shared secret derivation, and message encryption/decryption.
ApeRAG: Production-ready GraphRAG with multi-modal indexing, AI agents, MCP support, and scalable K8s deployment
This repository contains a directory of all official NetApp MCP servers.
A JSON-native lambda calculus runtime with metacircular evaluation, designed as an MCP (Model Context Protocol) server. Enables language models to evolve tools dynamically through metaprogramming.
Continuously updated catalogue of open-source OSINT tools, skills, plugins, MCP servers, and agentic integrations.
GoReleaser MCP Model Context Protocol server for GoReleaser.
Real-time Slack ingest and MCP server to power your agentic Slack bots
Agent Skills
The main repository for libraries used to build applications on the Atsign Platform. Three categories: SDKs, libraries, and Flutter widgets.
is an API-first Ruby web framework that combines the developer experience of Rails with fiber-based concurrency.
本项目是基于吴恩达老师在 DeepLearning.AI 平台推出的 agent-skills-with-anthropic 系列课程的中文学习资料整理项目。我们致力于为中文学习者提供高质量的课程内容翻译、系统的知识点梳理以及详细的示例代码解读，帮助大家更轻松地掌握 Agent Skills。
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upfetch is an advanced fetch client builder with standard schema validation, automatic response parsing, smart defaults and more.
A collection of Pi Coding Agent customized instances. Why? To showcase what it looks like to hedge against the leader in the agentic coding market, Claude Code.
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SkillsBench evaluates how well skills work and how effective agents are at using them.
Website • Getting Started • Documentation • Community
KAI Scheduler is a robust, efficient, and scalable Kubernetes scheduler that optimizes GPU resource allocation for AI and machine learning workloads.
An interactive five-chapter night walk through a Kyoto mountain temple, rendered live in Three.js.
For more advanced capabilities and end-to-end machine learning, visit www.k-dense.ai.